TY - GEN
T1 - Scene reconstruction using MRF optimalization with image content, adaptive energy functions
AU - Li, Ping
AU - Klein Gunnewiek, R.
AU - With, de, P.H.N.
PY - 2008
Y1 - 2008
N2 - Multi-view scene reconstruction from multiple uncalibrated images can be solved by two stages of processing: first, a sparse reconstruction using Structure From Motion (SFM), and second, a surface reconstruction using optimization of Markov random field (MRF). This paper focuses on the second step, assuming that a set of sparse feature points have been reconstructed and the cameras have been calibrated by SFM. The multi-view surface reconstruction is formulated as an image-based multi-labeling problem solved using MRF optimization via graph cut. First, we construct a 2D triangular mesh on the reference image, based on the image segmentation results provided by an existing segmentation process. By doing this, we expect that each triangle in the mesh is well aligned with the object boundaries, and a minimum number of triangles are generated to represent the 3D surface. Second, various objective and heuristic depth cues such as the slanting cue, are combined to define the local penalty and interaction energies. Third, these local energies are adapted to the local image content, based on the results from some simple content analysis techniques. The experimental results show that the proposed method is able to well the preserve the depth discontinuity because of the image content adaptive local energies.
AB - Multi-view scene reconstruction from multiple uncalibrated images can be solved by two stages of processing: first, a sparse reconstruction using Structure From Motion (SFM), and second, a surface reconstruction using optimization of Markov random field (MRF). This paper focuses on the second step, assuming that a set of sparse feature points have been reconstructed and the cameras have been calibrated by SFM. The multi-view surface reconstruction is formulated as an image-based multi-labeling problem solved using MRF optimization via graph cut. First, we construct a 2D triangular mesh on the reference image, based on the image segmentation results provided by an existing segmentation process. By doing this, we expect that each triangle in the mesh is well aligned with the object boundaries, and a minimum number of triangles are generated to represent the 3D surface. Second, various objective and heuristic depth cues such as the slanting cue, are combined to define the local penalty and interaction energies. Third, these local energies are adapted to the local image content, based on the results from some simple content analysis techniques. The experimental results show that the proposed method is able to well the preserve the depth discontinuity because of the image content adaptive local energies.
U2 - 10.1007/978-3-540-88458-3_79
DO - 10.1007/978-3-540-88458-3_79
M3 - Conference contribution
SN - 978-3-540-88457-6
T3 - Lecture Notes in Computer Science
SP - 872
EP - 882
BT - Advanced concepts for intelligent vision systems : 10th international conference, ACIVS 2008, Juan-les-Pins, France, October 20-24, 2008 ; proceedings
A2 - Blanc-Talon, Jacques
A2 - Bourennane, Salah
A2 - Philips, Wilfried
PB - Springer
CY - Berlin
ER -